Factor analysis of dynamic sequences (“factor analysis”) is a powerful technique for the analysis of dynamic sequences. However, with factor analysis, different initial conditions can lead to different solutions. Several techniques have been developed that address this problem, and improve the results of factor analysis. Some of these techniques, in particular those based on the use of a priori physiological information, may be tailored for a particular type of clinical study. These techniques generally require modification when used in different settings. For example, a particular factor analysis approach might yield satisfactory results for studies of healthy people but might not work for studies of patients with different degrees of ischemia, different ejection fractions and, therefore, large differences in the shape and amplitude of blood flow time activity curves, also known as “activity curves” or “factors.”
Other techniques address non-uniqueness of factor analysis solutions by minimizing a single objective function that penalizes the overlaps between factor images. Although these techniques increase the range of situations in which unique factor analysis solutions are achieved, in some situations they do not ensure a unique solution. For example, there are some situations where complete overlap of the resulting images of the factors, referred to herein as “factor images,” can prevent uniqueness. This is, however, very unlikely in cardiac imaging as the left and right ventricles are spatially disjoint.
Some major challenges of factor analysis include improving spatial resolution of images, estimating activity curves from noisy data without arterial blood sampling, and assessing absolute myocardial blood flow and coronary flow reserve.